_____________________________________________________________________________________________________ *Corresponding author: E-mail: bibi.muhammed@gmail.com, bibi.muhamme@gmail.com; Journal of Geography, Environment and Earth Science International 14(4): 1-10, 2018; Article no.JGEESI.40466 ISSN: 2454-7352 Modelling Social Vulnerability to Malaria Risk in Katsina-Ala Local Government Area, Benue State Nigeria Hundu T. Williams 1 and Bibi, Umar Muhammed 2* 1 Department of Geography, Adamawa State University, Mubi, P.M.B. 25, Nigeria. 2 Department of Geography, Federal University Kashere, P.M.B. 0182, Gombe, Nigeria. Authors’ contributions This work was carried out in collaboration between both authors. Author HTW conceived the topic and collected data from the field. Together with author BUM they designed the research outline, carried out analysis and wrote the manuscript. Both authors reviewed literature, read and consented to the final manuscript. Article Information DOI: 10.9734/JGEESI/2018/40466 Editor(s): (1) Teresa Lopez-Lara, Autonomous University of Queretaro, Qro, Mexico. Reviewers: (1) Reeves M. Fokeng, University of Bamenda, Cameroon. (2) Ratemo Sammy Kinara, Uganda. Complete Peer review History: http://www.sciencedomain.org/review-history/24198 Received 26 th January 2018 Accepted 8 th April 2018 Published 17 th April 2018 ABSTRACT Every society has certain groups of people who are more susceptible to risk due to lack of capacity to prevent it, which makes them vulnerable. Malaria is one of such infectious diseases that imposes a substantial burden on vulnerable populations. The objectives of this study is to map and analyze spatial pattern of sub-domains of social vulnerability to malaria risk in Katsina-Ala Local Government Area (LGA) of Benue State Nigeria, model and analyze areas of social vulnerability based on the sub-domains. Based on the review of related literature, a holistic risk and vulnerability framework was adopted to guide the assessment of social vulnerability to malaria risk in the study area. Stratified systematic non-aligned sampling technique was used to collect data on social vulnerability to malaria risk from three hundred and ten (310) households using structured questionnaire and GPS device. Empirical Bayesian Kriging model tool of Geostatistical analyst and Zonal statistical extension tools of ArcGis 10.2 were used for the model. Results revealed a heterogeneous spatial pattern of social vulnerability to malaria across the entire study area, Lack of capacity to anticipate Original Research Article